




import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__ :   Union[str, Any]						=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase__ :   List[str]						=							[]
for i in range(6):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append(
						    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
						)
						rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
						rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
						rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
						rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
						rename_keys.append(
						    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
						)
						rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
						rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
						rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
						# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
						)
						rename_keys.append(
						    (
						        F"""transformer.decoder.layers.{i}.cross_attn.out_proj.weight""",
						        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
						    )
						)
						rename_keys.append(
						    (
						        F"""transformer.decoder.layers.{i}.cross_attn.out_proj.bias""",
						        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
						    )
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

						# q, k, v projections in self/cross-attention in decoder for conditional DETR
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.weight""", F"""decoder.layers.{i}.sa_qcontent_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.weight""", F"""decoder.layers.{i}.sa_kcontent_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_qpos_proj.weight""", F"""decoder.layers.{i}.sa_qpos_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_kpos_proj.weight""", F"""decoder.layers.{i}.sa_kpos_proj.weight""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.weight""", F"""decoder.layers.{i}.sa_v_proj.weight"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.weight""", F"""decoder.layers.{i}.ca_qcontent_proj.weight""")
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.weight""", F"""decoder.layers.{i}.ca_kcontent_proj.weight""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_kpos_proj.weight""", F"""decoder.layers.{i}.ca_kpos_proj.weight""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.weight""", F"""decoder.layers.{i}.ca_v_proj.weight"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight""", F"""decoder.layers.{i}.ca_qpos_sine_proj.weight""")
						)

						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_qcontent_proj.bias""", F"""decoder.layers.{i}.sa_qcontent_proj.bias""")
						)
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.sa_kcontent_proj.bias""", F"""decoder.layers.{i}.sa_kcontent_proj.bias""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.sa_qpos_proj.bias""", F"""decoder.layers.{i}.sa_qpos_proj.bias"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.sa_kpos_proj.bias""", F"""decoder.layers.{i}.sa_kpos_proj.bias"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.sa_v_proj.bias""", F"""decoder.layers.{i}.sa_v_proj.bias"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_qcontent_proj.bias""", F"""decoder.layers.{i}.ca_qcontent_proj.bias""")
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_kcontent_proj.bias""", F"""decoder.layers.{i}.ca_kcontent_proj.bias""")
						)
						rename_keys.append((F"""transformer.decoder.layers.{i}.ca_kpos_proj.bias""", F"""decoder.layers.{i}.ca_kpos_proj.bias"""))
						rename_keys.append((F"""transformer.decoder.layers.{i}.ca_v_proj.bias""", F"""decoder.layers.{i}.ca_v_proj.bias"""))
						rename_keys.append(
						    (F"""transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias""", F"""decoder.layers.{i}.ca_qpos_sine_proj.bias""")
						)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)





def        __UpperCAmelCase						(							lowerCamelCase_ :     Union[str, Any]  ,							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     List[str]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      state_dict.pop(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      val





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  )       ->  Optional[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									SCREAMING_SNAKE_CASE_   : Tuple					  =      key.replace('backbone.0.body'  ,							'backbone.conv_encoder.model'  )
									SCREAMING_SNAKE_CASE_   : List[str]					  =      value
						else:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      value

			return new_state_dict





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     Any=False  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Tuple					  =      ''
			if is_panoptic:
						SCREAMING_SNAKE_CASE_   : Any					  =      'conditional_detr.'

			# first: transformer encoder
			for i in range(6  ):
						# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
						SCREAMING_SNAKE_CASE_   : Dict					  =      state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'  )
						# next, add query, keys and values (in that order) to the state dict
						SCREAMING_SNAKE_CASE_   : Tuple					  =      in_proj_weight[:2_56, :]
						SCREAMING_SNAKE_CASE_   : Tuple					  =      in_proj_bias[:2_56]
						SCREAMING_SNAKE_CASE_   : Tuple					  =      in_proj_weight[2_56:5_12, :]
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      in_proj_bias[2_56:5_12]
						SCREAMING_SNAKE_CASE_   : str					  =      in_proj_weight[-2_56:, :]
						SCREAMING_SNAKE_CASE_   : Any					  =      in_proj_bias[-2_56:]





def        __UpperCAmelCase						(							)       ->  Optional[int]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[str]					  =      'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      Image.open(requests.get(lowerCamelCase_  ,							stream=lowerCamelCase_  ).raw  )

			return im





@torch.no_grad()
def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     List[Any]  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[Any]					  =      ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						SCREAMING_SNAKE_CASE_   : int					  =      'resnet101'
			if "dc5" in model_name:
						SCREAMING_SNAKE_CASE_   : Dict					  =      True
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      'panoptic' in model_name
			if is_panoptic:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      2_50
			else:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      91
						SCREAMING_SNAKE_CASE_   : Dict					  =      'huggingface/label-files'
						SCREAMING_SNAKE_CASE_   : int					  =      'coco-detection-id2label.json'
						SCREAMING_SNAKE_CASE_   : int					  =      json.load(open(hf_hub_download(lowerCamelCase_  ,							lowerCamelCase_  ,							repo_type='dataset'  )  ,							'r'  )  )
						SCREAMING_SNAKE_CASE_   : List[str]					  =      {int(lowerCamelCase_  ): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_   : str					  =      idalabel
						SCREAMING_SNAKE_CASE_   : Any					  =      {v: k for k, v in idalabel.items()}

			# load image processor
			SCREAMING_SNAKE_CASE_   : int					  =      'coco_panoptic' if is_panoptic else 'coco_detection'
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      ConditionalDetrImageProcessor(format=lowerCamelCase_  )

			# prepare image
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      prepare_img()
			SCREAMING_SNAKE_CASE_   : int					  =      image_processor(images=lowerCamelCase_  ,							return_tensors='pt'  )
			SCREAMING_SNAKE_CASE_   : str					  =      encoding['pixel_values']

			logger.info(F'Converting model {model_name}...'  )

			# load original model from torch hub
			SCREAMING_SNAKE_CASE_   : str					  =      torch.hub.load('DeppMeng/ConditionalDETR'  ,							lowerCamelCase_  ,							pretrained=lowerCamelCase_  ).eval()
			SCREAMING_SNAKE_CASE_   : Any					  =      conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									SCREAMING_SNAKE_CASE_   : int					  =      'conditional_detr.' + src
						rename_key(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Any					  =      rename_backbone_keys(lowerCamelCase_  )
			# query, key and value matrices need special treatment
			read_in_q_k_v(lowerCamelCase_  ,							is_panoptic=lowerCamelCase_  )
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			SCREAMING_SNAKE_CASE_   : List[str]					  =      'conditional_detr.model.' if is_panoptic else 'model.'
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith('conditional_detr'  )
									    and not key.startswith('class_labels_classifier'  )
									    and not key.startswith('bbox_predictor'  )
									):
												SCREAMING_SNAKE_CASE_   : Tuple					  =      state_dict.pop(lowerCamelCase_  )
												SCREAMING_SNAKE_CASE_   : Tuple					  =      val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												SCREAMING_SNAKE_CASE_   : str					  =      state_dict.pop(lowerCamelCase_  )
												SCREAMING_SNAKE_CASE_   : List[str]					  =      val
									elif key.startswith('bbox_attention'  ) or key.startswith('mask_head'  ):
												continue
									else:
												SCREAMING_SNAKE_CASE_   : List[str]					  =      state_dict.pop(lowerCamelCase_  )
												SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      val
						else:
									if not key.startswith('class_labels_classifier'  ) and not key.startswith('bbox_predictor'  ):
												SCREAMING_SNAKE_CASE_   : Tuple					  =      state_dict.pop(lowerCamelCase_  )
												SCREAMING_SNAKE_CASE_   : str					  =      val
    # finally, create HuggingFace model and load state dict
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      ConditionalDetrForSegmentation(lowerCamelCase_  ) if is_panoptic else ConditionalDetrForObjectDetection(lowerCamelCase_  )
			model.load_state_dict(lowerCamelCase_  )
			model.eval()
			model.push_to_hub(repo_id=lowerCamelCase_  ,							organization='DepuMeng'  ,							commit_message='Add model'  )
			# verify our conversion
			SCREAMING_SNAKE_CASE_   : str					  =      conditional_detr(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Any					  =      model(lowerCamelCase_  )
			assert torch.allclose(outputs.logits  ,							original_outputs['pred_logits']  ,							atol=1E-4  )
			assert torch.allclose(outputs.pred_boxes  ,							original_outputs['pred_boxes']  ,							atol=1E-4  )
			if is_panoptic:
						assert torch.allclose(outputs.pred_masks  ,							original_outputs['pred_masks']  ,							atol=1E-4  )

			# Save model and image processor
			logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'  )
			Path(lowerCamelCase_  ).mkdir(exist_ok=lowerCamelCase_  )
			model.save_pretrained(lowerCamelCase_  )
			image_processor.save_pretrained(lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[int]						=							argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_name''',
						    default='''conditional_detr_resnet50''',
						    type=str,
						    help='''Name of the CONDITIONAL_DETR model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
						)
						UpperCamelCase__ :   Optional[int]						=							parser.parse_args()
						convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
	import torch

	from transformers import (
	    MraForMaskedLM,
	    MraForMultipleChoice,
	    MraForQuestionAnswering,
	    MraForSequenceClassification,
	    MraForTokenClassification,
	    MraModel,
	)
	from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=8,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=99,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=5,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=36,   SCREAMING_SNAKE_CASE_="gelu",   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=512,   SCREAMING_SNAKE_CASE_=16,   SCREAMING_SNAKE_CASE_=2,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=None,   )       ->   str:
						UpperCAmelCase_:		int							      =     parent
						UpperCAmelCase_:		Dict							      =     batch_size
						UpperCAmelCase_:		Optional[int]							      =     seq_length
						UpperCAmelCase_:		int							      =     is_training
						UpperCAmelCase_:		List[Any]							      =     use_input_mask
						UpperCAmelCase_:		int							      =     use_token_type_ids
						UpperCAmelCase_:		Tuple							      =     use_labels
						UpperCAmelCase_:		Tuple							      =     vocab_size
						UpperCAmelCase_:		int							      =     hidden_size
						UpperCAmelCase_:		List[str]							      =     num_hidden_layers
						UpperCAmelCase_:		List[str]							      =     num_attention_heads
						UpperCAmelCase_:		Any							      =     intermediate_size
						UpperCAmelCase_:		str							      =     hidden_act
						UpperCAmelCase_:		Optional[int]							      =     hidden_dropout_prob
						UpperCAmelCase_:		Optional[Any]							      =     attention_probs_dropout_prob
						UpperCAmelCase_:		List[str]							      =     max_position_embeddings
						UpperCAmelCase_:		Optional[int]							      =     type_vocab_size
						UpperCAmelCase_:		Tuple							      =     type_sequence_label_size
						UpperCAmelCase_:		Tuple							      =     initializer_range
						UpperCAmelCase_:		str							      =     num_labels
						UpperCAmelCase_:		str							      =     num_choices
						UpperCAmelCase_:		Optional[int]							      =     scope

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size					)

						UpperCAmelCase_:		List[str]							      =     None
						if self.use_input_mask:
									UpperCAmelCase_:		Union[str, Any]							      =     random_attention_mask([self.batch_size, self.seq_length]					)

						UpperCAmelCase_:		List[str]							      =     None
						if self.use_token_type_ids:
									UpperCAmelCase_:		str							      =     ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size					)

						UpperCAmelCase_:		str							      =     None
						UpperCAmelCase_:		List[Any]							      =     None
						UpperCAmelCase_:		Tuple							      =     None
						if self.use_labels:
									UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size],   self.type_sequence_label_size					)
									UpperCAmelCase_:		str							      =     ids_tensor([self.batch_size, self.seq_length],   self.num_labels					)
									UpperCAmelCase_:		Tuple							      =     ids_tensor([self.batch_size],   self.num_choices					)

						UpperCAmelCase_:		List[Any]							      =     self.get_config()

						return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

			def      __snake_case       (self					)       ->   List[Any]:
						return MraConfig(
						    vocab_size=self.vocab_size,   hidden_size=self.hidden_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   max_position_embeddings=self.max_position_embeddings,   type_vocab_size=self.type_vocab_size,   is_decoder=SCREAMING_SNAKE_CASE_,   initializer_range=self.initializer_range,   )

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Optional[Any]							      =     self.get_config()
						UpperCAmelCase_:		Dict							      =     300
						return config

			def      __snake_case       (self					)       ->   List[Any]:
						(
						    (
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							
						):		Any							      =     self.prepare_config_and_inputs()

						UpperCAmelCase_:		List[str]							      =     True
						UpperCAmelCase_:		Any							      =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
						UpperCAmelCase_:		List[str]							      =     ids_tensor([self.batch_size, self.seq_length],   vocab_size=2					)

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[int]							      =     MraModel(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   Union[str, Any]:
						UpperCAmelCase_:		Any							      =     True
						UpperCAmelCase_:		Union[str, Any]							      =     MraModel(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Optional[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   encoder_hidden_states=SCREAMING_SNAKE_CASE_,   encoder_attention_mask=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		List[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   encoder_hidden_states=SCREAMING_SNAKE_CASE_,   )
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		int							      =     MraForMaskedLM(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		int							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						UpperCAmelCase_:		Any							      =     MraForQuestionAnswering(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		str							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   start_positions=SCREAMING_SNAKE_CASE_,   end_positions=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.start_logits.shape,   (self.batch_size, self.seq_length)					)
						self.parent.assertEqual(result.end_logits.shape,   (self.batch_size, self.seq_length)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						UpperCAmelCase_:		List[str]							      =     self.num_labels
						UpperCAmelCase_:		Any							      =     MraForSequenceClassification(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Tuple							      =     self.num_labels
						UpperCAmelCase_:		Any							      =     MraForTokenClassification(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     self.num_choices
						UpperCAmelCase_:		int							      =     MraForMultipleChoice(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Dict							      =     input_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[str]							      =     token_type_ids.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[Any]							      =     input_mask.unsqueeze(1					).expand(-1,   self.num_choices,   -1					).contiguous()
						UpperCAmelCase_:		List[Any]							      =     model(
						    SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   token_type_ids=SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_,   )
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices)					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		int							      =     self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							
						):		Any							      =     config_and_inputs
						UpperCAmelCase_:		Dict							      =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict







@require_torch
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(
			    (
			        MraModel,
			        MraForMaskedLM,
			        MraForMultipleChoice,
			        MraForQuestionAnswering,
			        MraForSequenceClassification,
			        MraForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			A      		=	False
			A      		=	False
			A      		=	False
			A      		=	False

			A      		=	()

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Dict							      =     MraModelTester(self					)
						UpperCAmelCase_:		List[Any]							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_,   hidden_size=37					)

			def      __snake_case       (self					)       ->   Tuple:
						self.config_tester.run_common_tests()

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		int							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
									UpperCAmelCase_:		str							      =     type
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		str							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		str							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_					)

			@slow
			def      __snake_case       (self					)       ->   Any:
						for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_:		List[str]							      =     MraModel.from_pretrained(SCREAMING_SNAKE_CASE_					)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)

			@unittest.skip(reason="""MRA does not output attentions"""					)
			def      __snake_case       (self					)       ->   List[str]:
						return







@require_torch
class 				_a				(   unittest.TestCase					):

			@slow
			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     MraModel.from_pretrained("""uw-madison/mra-base-512-4"""					)
						UpperCAmelCase_:		Optional[Any]							      =     torch.arange(256					).unsqueeze(0					)

						with torch.no_grad():
									UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		Optional[int]							      =     torch.Size((1, 256, 768)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		int							      =     torch.tensor(
						    [[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]]					)

						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)

			@slow
			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		List[str]							      =     MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""					)
						UpperCAmelCase_:		Dict							      =     torch.arange(256					).unsqueeze(0					)

						with torch.no_grad():
									UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		Any							      =     50265

						UpperCAmelCase_:		Any							      =     torch.Size((1, 256, vocab_size)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Dict							      =     torch.tensor(
						    [[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]]					)

						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)

			@slow
			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Optional[int]							      =     MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""					)
						UpperCAmelCase_:		List[Any]							      =     torch.arange(4096					).unsqueeze(0					)

						with torch.no_grad():
									UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_					)[0]

						UpperCAmelCase_:		Dict							      =     50265

						UpperCAmelCase_:		int							      =     torch.Size((1, 4096, vocab_size)					)
						self.assertEqual(output.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[Any]							      =     torch.tensor(
						    [[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]]					)

						self.assertTrue(torch.allclose(output[:, :3, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)






